Fault detection based on seismic data interpretation

ABSTRACT

A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

BACKGROUND

The disclosure generally relates to the field of subsurface faultdetection, and more particularly to subsurface fault detection based onseismic data interpretation.

Interpretation of seismic data enhances understanding of subsurfacegeological features in a formation (e.g., faults, fractures, groups offractures, porous regions, etc.). These seismic interpretations canprovide the position and shape of these subsurface geological features.The position and shape of these subsurface geological features areuseful to optimizing hydrocarbon production during drilling andstimulation treatments. For example, drilling location, various drillingparameters, production parameters, drilling project characterization andranking, etc. can be determined based on knowledge of the position andshape of these subsurface geological features. Increasing the accuracyand speed of seismic interpretation through the use of faultinterpretation algorithms increases the efficiency, economy, and safetyof drilling and stimulation operations.

The complexity of seismic data results in fault interpretation workflowsthat include several operations which involve significant manual effortand/or a substantial number of parameters. Moreover, each operation caninvolve a significant amount of human input, such as testing manydifferent parameters in these algorithms to determine their effects,classifying several types of detected features, and verifying that analgorithm is accurate during post-processing. These factors can increasethe time and computing cost of performing a seismic interpretation andreduce the accuracy of the resulting interpretations.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure can be better understood by referencingthe accompanying drawings.

FIG. 1 depicts a schematic diagram of an elevation view of a typicalmarine seismic survey that can be used to provide seismic data.

FIG. 2 depicts a schematic diagram of an onshore borehole seismic surveyenvironment.

FIG. 3 depicts a flowchart of operations to generate an interpretedseismic volume.

FIG. 4 depicts a flowchart of operations to use a convolutional neuralnetwork generate an interpreted seismic volume.

FIG. 5 depicts a seismic dataset and an edge-detected seismic dataset.

FIG. 6 depicts a 5×5 subsample of an edge-detected seismic dataset andcorresponding fault likelihood.

FIG. 7 depicts a neural network being applied onto a seismic dataset.

FIG. 8 depicts a convolutional neural network being applied onto aseismic dataset.

FIG. 9 depicts a workflow for an automated fault interpretation system.

FIG. 10 depicts a comparison between an expert-labeled seismic datasetand an automated fault interpretation system-labeled seismic dataset.

FIG. 11 depicts an example drilling system near a fault.

FIG. 12 depicts an example wellbore system near a fault.

FIG. 13 depicts an example computer system.

DESCRIPTION

The description that follows includes example systems, methods,techniques, and program flows that embody embodiments of the disclosure.However, it is understood that this disclosure can be practiced withoutthese specific details. For instance, this disclosure refers toconvolutional neural networks. Aspects of this disclosure can be insteadapplied to other machine-learning operations, such as traditional neuralnetworks, backpropagation neural networks, and recurrent neuralnetworks. In other instances, well-known instruction instances,protocols, structures and techniques have not been shown in detail inorder not to obfuscate the description.

Various embodiments relate to an automated fault interpretation systemthat provides seismic interpretations. The automated faultinterpretation system can be based on a deep learning faultinterpretation method that can include a deep learning basedclassification algorithm to identify geological features such asgeological faults (“faults”) from seismic volumes based on a set ofedge-detected datasets. The methods allow the automated processing ofever-larger volumes of seismic data with greater efficiency andaccuracy.

In some embodiments, the deep learning fault interpretation methodincludes an edge detection method and a neural network method. Afterconverting wave-based seismic datasets to a spatially digitized datasetsuch as a pixel-based dataset, the spatially digitized dataset isprocessed with an edge detection method to generate an edge-detecteddataset. The edge detection method can determine a set of indicators ofcandidate discontinuities in a formation from the spatially digitizeddataset and incorporate this set of indicators of candidatediscontinuities into the edge-detected dataset. An indicator of afeature (e.g. a candidate discontinuity) is a representation of thefeature (e.g. a data object, an array of values, set of functions,combinations thereof, etc.) and can be included in a dataset. Forexample, an indicator of a discontinuity can be a data object having aset of pixel values that correspond with the discontinuity and includeinformation about the discontinuity such as its position, size, and/orshape. Determining a set of indicators of candidate discontinuitiesincludes determining an indicator corresponding with its respectivecandidate discontinuity at least one of a position, shape, andorientation of each candidate discontinuity in the respective set ofcandidate discontinuities. Candidate discontinuities can include actualdiscontinuities in a formation (i.e. physical separations in materialsor formation layers) such as faults and fractures. These faults and/orfractures can be target discontinuities of interest. Candidatediscontinuities can also include features such as signal reflectors.Signal reflectors can be candidate discontinuities and incorporated intothe edge-detected dataset by an edge-detection algorithm when the signalreflectors have strong discontinuity signals. A neural network and/ordeep neural network (i.e. a neural network with multiple layers betweenan input and output) can be applied on the edge-detected dataset toperform fault likelihood labeling.

Fault likelihood labeling can include labeling a subset of indicators ofdiscontinuities from the set of indicators of candidate discontinuitiesof the edge-detected dataset to filter out non-targeted discontinuitiesand keep target discontinuities such as faults and fractures to generatea filtered, edge-detected dataset. Non-targeted discontinuities can bedefined as any discontinuity that is not a target discontinuity and canchange depending on what is selected as a target discontinuity. Forexample, a signal reflector can be a non-targeted discontinuity if thesignal reflector is not a target discontinuity. The label candistinguish a candidate discontinuity as being a target discontinuity ornot being a target discontinuity. The deep learning fault interpretationmethod can be applied on any number of seismic datasets to provideseismic interpretation results that efficiently and accurately revealgeological features such as fractures and faults. In some cases, aneural network can be used to detect alternative target features such asimaging artifacts (e.g. reflectors and signal outliers) and includethese alternative target features in a seismic interpretation result. Insome embodiments, these seismic interpretation results can be performedin real-time or briefly after acquisition of a seismic dataset (e.g.within ten minutes of measuring or acquiring a seismic dataset tointerpret).

In some embodiments, the seismic interpretation results can be used todetermine a position of a geological feature associated with one of thetarget discontinuities based on the labeled subset of indicators ofdiscontinuities in the seismic interpretation results. These positionscan be used to plan drilling direction or well stimulation treatments.For example, the positions of faults can be used to determine a drillingplan to ensure that drilling does not drill into a fault. In someembodiments, the position of the fault can be incorporated into adrilling control system to automatically prevent a drill from drillingnear a boundary close to the fault plane. Additionally, the position offaults can determine the parameters of a stimulation treatment so thatstimulation would not damage or perforate geological media in thevicinity of a fault.

EXAMPLE SEISMIC DATA ACQUISITION SYSTEM

FIG. 1 depicts a schematic diagram of an elevation view of a typicalmarine seismic survey that can be used to provide seismic data. A bodyof water 101 over the earth 102 is bounded at a water surface 103 by awater-air interface and at a water bottom 104 by a water-earthinterface. Beneath the water bottom 104, the earth 102 containssubterranean formations of interest. A seismic vessel 105 travels on thewater surface 103 and contains seismic acquisition control equipment106. The seismic acquisition control equipment 106 includes navigationcontrol, seismic source control, seismic sensor control, and recordingequipment.

The seismic acquisition control equipment 106 causes a seismic source107 towed in the body of water 101 by a seismic vessel 105 to actuate atselected times. Seismic streamers 108 contain sensors to detect thereflected waves initiated by the seismic source 107 and reflected frominterfaces in the environment. The seismic streamers 108 can containpressure sensors such as hydrophones 109 and/or water particle motionsensors such as geophones 110. The hydrophones 109 and geophones 110 aretypically co-located in pairs or pairs of sensor arrays at regularintervals along the seismic streamers 108.

The seismic source 107 is activated at periodic intervals to emitacoustic waves in the vicinity of the seismic streamers 108 with thehydrophones 109 and the geophones 110. Each time the seismic source 107is actuated, an acoustic wave travels upwardly or downwardly inspherically expanding wave fronts. The traveling waves will beillustrated by ray paths normal to the expanding wave fronts. Thedownwardly traveling wave from the seismic source 107 traveling along aray path 113 will reflect off the earth-water interface at the waterbottom 104 and then travel upwardly along ray path 114, where the wavecan be detected by the hydrophones 109 and geophones 110. Such areflection at the water bottom 104, as in ray path 114, containsinformation about the water bottom 104 and hence can be retained forfurther processing. Additionally, the downwardly traveling wavetraveling along ray path 113 can transmit through the water bottom 104and travel along ray path 115 before reflecting off a layer boundary116. This wave can then travel upwardly along ray path 117 and bedetected by the hydrophones 109 and geophones 110. Such a reflection offthe layer boundary 116 can contain useful information about subterraneanformations of interest that can be used to generate seismic data.

FIG. 2 depicts a schematic diagram of an onshore borehole seismic surveyenvironment. Seismic receivers 202 are in a spaced-apart arrangementwithin a borehole 203 to detect seismic waves. As shown, the seismicreceivers 202 can be fixed in place by anchors 204 to facilitate sensingseismic waves. In different embodiments, the seismic receivers 202 canbe part of a logging-while-drilling (LWD) tool string or wirelinelogging tool string. Further, the seismic receivers 202 communicatewirelessly or via cable to a data acquisition unit 206 at a surface 205,where the data acquisition unit 206 receives, processes, and storesseismic signal data collected by the seismic receivers 202. To generateseismic signal data, surveyors trigger a seismic source 208 at one ormore positions to generate seismic energy waves that propagate through aformation 210. Such waves reflect from acoustic impedancediscontinuities to reach the seismic receivers 202. Illustrativediscontinuities include faults, boundaries between formation beds, andboundaries between formation fluids. The discontinuities can appear asbright spots in the subsurface structure representation that is derivedfrom the seismic signal data. The collected seismic signal data can beused to generate a seismic dataset.

EXAMPLE OPERATIONS

FIG. 3 depicts a flowchart of operations to generate an interpretedseismic volume. Operations of the flowchart 300 begin at block 302.Operations of the flowchart 300 can be performed with by a systemcomprising a processor.

At block 302, a seismic dataset is acquired and processed. The seismicdataset can be a multi-dimensional dataset based on signals/values ofseismic sensors that can receive waves generated from a source (e.g.,seismic source 107 from FIG. 1 or seismic source 208 from FIG. 2) andreflected from within a formation. In some embodiments, the seismicdatasets can be processed to convert the dataset from a set of seismicreflection data, such as a “SEGY” data format, into a spatiallydigitized data format such as a pixel-based data format. For example,the seismic dataset can be initially received as a set of amplitudevalues of each of a set of two-dimensional seismic cross-sections (e.g.inline cross-sections and crossline cross-sections that can be stackedto form a formation volume). The seismic dataset can be processed byusing a bijective mapping. In this process, a bijective mapping is builtbetween each of the set of amplitude values and a pixel value in animage. Alternatively, some embodiments can include converting an initialseismic dataset into a three-dimensional spatially digitized datasetsuch as a three-dimensional voxel-based dataset.

At block 304, a band-pass filter is applied to the seismic dataset. Somecandidate discontinuity capture (i.e. capture of signals used todetermine indicators of candidate discontinuities as indicators) can beoptimized using signals at a particular frequency. For example, certaincandidate discontinuities in a seismic dataset can be captured moreaccurately by high frequency signals. A set of bandpass filters can beapplied to the collected seismic signals to isolate or capture greaterdetail on candidate discontinuities such as fractures and faults. Forexample, a set of bandpass filters including a low bandpass filter witha frequency of 5-10 Hz and a high bandpass filter with a frequency of50-70 Hz can be applied to the collected seismic dataset(s). Use of alow bandpass filter with a frequency of 5-10 Hz on a seismic dataset canprovide better candidate discontinuity capture of larger candidatediscontinuities. Use of a high bandpass filter with a frequency of 50-70Hz on a seismic dataset can provide better candidate discontinuitycapture for smaller candidate discontinuities.

At block 308, an edge-detected seismic dataset having a set ofindicators of candidate discontinuities is determined based on theseismic dataset using edge detection. The edge detection can include theapplication of a phase congruency operation on the seismic dataset. Forexample, a pixel-based phase congruency operation can identify cornersand edges from pixel images. These identified corners and edges areuseful for detecting features such as candidate discontinuities anddetermining the indicators of the candidate discontinuities. Inaddition, the phase congruency operation can also capture reflectors,which can have a clear edge, and determine indicators of thesereflectors. In some embodiments, the phase congruency operation canfilter out continuous events such as structural and stratigraphicfeatures in the seismic data. In some embodiments, the phase congruencyoperation can determine a set of indicators of candidate discontinuitiesthat could be missed by other discontinuity detection methods such asmachine learning-based discontinuity detection algorithms.Alternatively, for three-dimensional voxel-based seismic datasets, otheredge-detection methods can be used to detect candidate discontinuitiesin a three-dimensional model of the formation. For example, an edgedetection method can include comparing the n-nearest neighboring valuesof a voxel to determine if the voxel is at an edge or not at an edge.

At block 312, seismic subsamples are generated by partitioning theedge-detected seismic dataset. The size of a seismic dataset, such as aseismic volume or image, can be significant and cost an inordinateamount of time or computing resources to properly train in a neuralnetwork. Thus, an operation can partition an edge-detected seismicdataset into seismic subsamples to more efficiently train a neuralnetwork to perform fault likelihood labeling. For example, an operationcan partition an edge-detected seismic dataset into seismic subsamples,each seismic subsample including a 5×5 pixel image along with anycorresponding pre-calculated labels. In alternative embodiments, thedimensions of the labeling array can have any arbitrary dimensions, suchas 2 pixels by 4 pixels, 10 pixels by 10 pixels, or 30 pixels by 20pixels. In alternative three-dimensional embodiments, such as oneswherein the seismic dataset is formed from three-dimensional voxels, alabeling array can also have three dimensions. For example, the labelingarray can have any arbitrarily-sized dimensions smaller than the size ofa full seismic dataset, such as 2×2×2 pixels, 3×3×2 pixels, or 10×20×25pixels.

At block 316, a determination is made of whether a combined value of aseismic subsample is greater than a threshold value. The determinationcan be made as part of a binary classification operation (i.e. operationthat labels/classifies a feature into one of only two categories) inorder to increase efficiency in a neural network filter. In someembodiments, a combined value for an entire subsampled volume or imagecan be assigned based on all the labels/values of its constituentindicator elements. For example, a seismic subsample can include a 5×5pixel array and can have a corresponding 5×5 subsample labeling array,wherein each element of the 5×5 subsample labeling array has a valuebetween zero and one to represent the likelihood of a pixelcorresponding with the labeling element actually representing a part ofa candidate discontinuity. The combined value of the seismic subsamplecan be the arithmetic mean of each of the subsample labeling arrays.Alternatively, the combined value of the seismic subsample can be aweighted mean of the subsample labeling array, median value of thesubsample labeling array, or random value drawn from one of the elementsof the subsample labeling array. The combined value can then be comparedto a threshold value such as 0.5 to determine if the combined value isgreater than the threshold. In alternative embodiments, instead ofdetermining whether a value is greater than or equal to a threshold, thecriterion can be whether a value is less than or equal to a threshold.In the case that the combined is greater than the threshold value,operations of the flowchart 300 proceed to block 320. Otherwise,operations proceed to block 324.

At block 320, the seismic subsample is assigned as representing a partof a candidate discontinuity. Being assigned as representing a part of acandidate discontinuity can include explicitly changing a tag oridentifier corresponding with the seismic subsample to reflect that theseismic subsample represents a part of a candidate discontinuity. Forexample, an array corresponding with the seismic subsamples can have anarray value assigned to a particular seismic subsample be set to “0” toreflect that the seismic subsample is assigned as a candidatediscontinuity. Alternatively, various tags, boolean values, oridentifiers can be used to assign the seismic subsample as representinga part of a candidate discontinuity (e.g., a boolean value of “false,” astring value of “discontinuity”, etc.).

At block 324, the seismic subsample is assigned as not representing apart of a candidate discontinuity. Being assigned as not representing apart of the candidate discontinuity can include explicitly changing atag or identifier corresponding with the seismic subsample to reflectthat the seismic subsample is not representing a part of the candidatediscontinuity. For example, an array of seismic subsamples can have thearray value assigned to the seismic subsample be “1” to reflect that theseismic subsample is not a discontinuity. Alternatively, various tags,boolean values, or identifiers can be used to assign the seismicsubsample as not representing a part of a discontinuity (e.g., a booleanvalue of “true,” a string value of “not discontinuity”, etc.).

At block 328, a determination is made of whether additional seismicsubsamples are available. If so, operations of the flowchart 300proceeds to the next available seismic subsample and returns to block316. Otherwise, operations of the flowchart 300 proceeds to block 332.

At block 332, a determination is made of whether a trained neuralnetwork is available. If a trained neural network is available,operations of the flowchart 300 proceed to block 340. Otherwise,operations of the flowchart 300 proceed to block 344.

At block 340, a subset of the indicators of discontinuities are labeledas indicators of target discontinuities using the trained neural networkbased on the set of seismic subsamples. The trained neural network canbe a deep neural network such as a convolutional neural network.Alternatively, the trained neural network can be another type offeedforward neural network such as a time delay neural network, radialbasis function neural network, or recurrent neural network. The trainedneural network can distinguish between target features (e.g. targetdiscontinuities) and non-targeted features and label featuresaccordingly. For example, the trained neural network can generate alabeled subset of indicators of discontinuities from a set of indicatorsof candidate discontinuities based on each of the labeled subset ofindicators of discontinuities being identified as being one of theindicators of target discontinuities. In some embodiments, theindicators of target discontinuities can be a part of an interpretedseismic volume that includes representations of various geologicalfeatures associated with the target discontinuities such as faults orfractures.

The indicator of the target discontinuity can be a part of aninterpreted seismic volume. The interpreted seismic volume can includean array that associates one or more positions in a geological regioncorresponding to the seismic dataset with the target discontinuity or ageological feature associated with the target discontinuity. Ageological feature associated with a target discontinuity can includethe entirety of the target discontinuity, a specific section of thetarget discontinuity, a group of discontinuities including the targetdiscontinuity, etc. The interpreted seismic volume can be de-noised toemphasize the position/orientation/shape of the target discontinuity ora geological feature associated with the target discontinuity. Forexample, a trained neural network can assign a “fracture/fault” or “notfracture/fault” label to each geological feature of a seismic dataset byassigning the label to each pixel of a two-dimensional dataset or eachvoxel of a three-dimensional dataset, and remove geological features notassigned as a fracture/fault from the interpreted seismic volume. Withrespect to FIG. 4, described further below, operations similar to or thesame as those described for blocks 404-416 can provide the interpretedseismic volume having the target discontinuity.

At block 344, the neural network is trained based on the set of seismicsub samples. The neural network can be trained using either or both realseismic datasets or synthetic/generated seismic datasets. In someembodiments, training datasets can be based on manual interpretation ofseismic data with fractures/faults labeled by human domain experts. Insome embodiments, training datasets can include software-generateddatasets and be based on fault likelihood algorithms such as asemblance-based algorithm. In some embodiments, the neural network canbe a supervised learning approach based on labeled training datasets.

FIG. 4 depicts a flowchart of operations to use a convolutional neuralnetwork generate an interpreted seismic volume. Operations of theflowchart 400 begin at block 404. Operations of the flowchart 400 can beperformed using a system comprising a processor. While the system canperform operations of the flowchart 400 before edge-detection of theseismic dataset, the system can also perform operations of the flowchart400 after edge-detection of the seismic dataset.

At block 404, one or more convolution layers are applied to a seismicdataset to generate a set of convoluted seismic subsamples. The seismicdataset can be an edge-detected seismic dataset having a set ofindicators of candidate discontinuities before application of the one ormore convolution layers. Alternatively, the seismic dataset can bedirectly processed by the one or more convolution layers before anedge-detection operation occurs. Applying a convolution layer includesapplying one or more convolutional filters to each of the set of seismicsubsamples. Applying the one or more convolutional filters to each ofthe set of seismic subsamples can include determining a dot productresult between the one or more convolutional filters and each respectiveseismic subsample of the set of seismic subsamples.

At block 408, the system applies pooling to generate a set ofdownsampled convoluted seismic data. Once one or more convolution layershave been applied, the system can pool the set of convoluted seismicsubsamples to increase the efficiency of other operations of theconvolutional neural network. Benefits of pooling a dataset can includereducing the spatial size of representation and control overfitting.

At block 412, a reduced dimensional vector for labeling is generated byapplying one or more Rectified Linear Units (ReLU) Layers and fullyconnected layers to the set of convoluted seismic subsamples. In someembodiments, use of a unit of a ReLU layer can include the use ofvarious activation functions such as a non-saturating activationfunction f(x)=max(0, x). Alternatively, a unit of a ReLU layer caninclude a saturating hyperbolic tangent function, sigmoid function, orother nonlinear function. After application of the ReLU layers, use ofone or more fully connected layers can provide a reduced dimensionalvector for labeling an indicator of a target discontinuity from a set ofindicators of candidate discontinuities. Additional layers, such as afinal layer of softmax units can be used to contribute to/improve thereduced dimensional vector for labeling indicators of targetdiscontinuities. For example, application of ReLU layers and a softmaxunits layer onto a set of convoluted seismic subsamples can provide areduced dimensional vector used to label indicators of faults in ageological formation and distinguish the indicators of faults fromindicators of reflectors in the geological formation.

At block 416, an interpreted seismic volume having the indicators oftarget discontinuities is generated based on the reduced dimensionalvector for labeling. Utilization of the reduced dimensional vector cangenerate a labeled subset of indicators of discontinuities by applyingthe reduced dimensional vector to the set of indicators of candidatediscontinuities. A system can combine the seismic dataset with theindicators of target discontinuities to generate an interpreted seismicvolume. The interpreted seismic volume can provide a physical positionof one or more target discontinuities in the geological formation, whichcan be used to plan drilling operations, well treatment operations, etc.For example, the interpreted seismic volume can include information onthe physical position of a fault in a geological formation with respectto a drill bit in the geological formation. This information can then beused to stop drilling activity when the drill bit is within a thresholddistance from the fault.

EXAMPLE DATA

FIG. 5 depicts a seismic dataset and an edge-detected seismic dataset.With respect to FIG. 3, the seismic dataset 502 can be processed usingan edge detection algorithm similar or the same as the one described forblock 308 to produce an edge-detected seismic dataset 504. Theedge-detected seismic datasets include indicators of various candidatediscontinuities such as an indicator of a reflector 510, an indicator ofa fault 512, and an indicator of a fracture 506. A trained neuralnetwork such as a trained deep neural network can use the edge-detectedseismic dataset 504 to determine which of the indicators of candidatediscontinuities are indicators of target discontinuities and which areindicators of non-target discontinuities.

FIG. 6 depicts a 5×5 subsample of an edge-detected seismic dataset andcorresponding fault likelihood. Training and processing a large pixelimage can have a significant numerical cost and raise classificationchallenges. One such challenge is that each fault segment is unique andit can be difficult to assign fault segments into a certain class. Forexample, with reference to FIG. 5, the edge-detected seismic dataset 504can have pixel dimensions of 1301 pixels by 1889 pixels and acorresponding label array of equal size (e.g., each array value in thelabel array representing either “true” or “false” based on whether ornot the corresponding pixel to the array value is detected as part of anindicator for a candidate discontinuity). The size of the edge detectedseismic dataset 504 can incur a significant computational cost forneural network operations. To reduce this computational cost, the edgedetected seismic dataset 504 can be partitioned into seismic subsamplessuch as the seismic subsample 602.

For example, with respect to FIG. 3, the seismic subsamples aregenerated using operations similar to or the same as those described inthe flowchart 300. For each of the seismic subsamples, the brightness ofeach pixel can be represented as a corresponding value in the 5×5subsample labeling array 604, wherein each element of the 5×5 subsamplelabeling array 604 has a value between zero and one to represent thelikelihood of the element's corresponding pixel representing a part of acandidate discontinuity. The 5×5 subsample labeling array 604 can beassigned a subset label for the entire 5×5 subsample labeling array 604.For example, a binary classification operation can be used with athreshold value of 0.5. The result of the binary classificationoperation can be compared with the arithmetic mean of the 5×5 subsamplelabeling array 604 to determine if the seismic subsample is to belabeled as representing a part of a target discontinuity (e.g. ageological fault) or not. In the case that the arithmetic mean isgreater than 0.5, the 5×5 pixel image 602 would be classified asrepresenting a part of a geological fault. Otherwise, the 5×5 pixelimage 602 would not be classified as representing a part of a geologicalfault.

FIG. 7 depicts a neural network being applied onto a seismic dataset.FIG. 7 depicts an example convolutional neural network process 700. Aseismic dataset such as the seismic dataset 702 can be partitioned intoseismic subsamples 704. Each of the seismic subsamples 704 can beedge-detected and then quantified or classified with a value. In someembodiments, the value is based on whether a mean or weighted-mean of afault-likelihood estimate for each element (e.g. pixel) in anedge-detected result of the seismic subsamples 704 is greater than orless than a threshold. In other embodiments, a value for each element inthe seismic subsamples 704 can be used directly as a training orvalidation set for the neural network 706.

The neural network 706 can generate an interpreted seismic dataset 708,wherein target discontinuities such as fractures and faults can beidentified. The seismic dataset 702 can be processed with anedge-detection algorithm before being processed by the neural network706. The result of the edge-detection algorithm can be used as an inputfor the neural network 706 to generate the interpreted seismic dataset708. In some embodiments, both the seismic dataset and its correspondingedge-detected seismic dataset can be used as inputs for the neuralnetwork 706 to generate the interpreted seismic dataset 708.

FIG. 8 depicts a convolutional neural network being applied onto aseismic dataset. FIG. 8 depicts an example convolutional neural networkprocess 800. The convolutional neural network processes the seismicdataset 802 into the pooled convoluted datasets 808. In someembodiments, the seismic dataset 802 can be processed by an edgedetection algorithm before being processed by the convolutional neuralnetwork. Each dataset of the pooled convoluted datasets 808 can begenerated by a distinct convolution filter, wherein each convoluteddataset can be based on convoluted samples. For example, a subset array804 can be convoluted by the convolution filter 805 into the convolutiondata subset 806, wherein the convolution data subset 806 is one of aseries of data subsets that form a dataset of the pooled convoluteddatasets 808. The pooled convoluted datasets 808 can then be downsampledinto the pooled downsampled convolution datasets 814 and assigned anappropriate subsample labeling array. For example, a convolution datasubset 810 can be reduced by downsampling filter 811 into thedownsampled convolution subsample 812.

Once the pooled downsampled convolution datasets 814 have beengenerated, the pooled downsampled convolution datasets 814 can beprocessed by one or more layers of activation function units such as theReLU layer 816. The output of the ReLU layer 816 can then be processedby a softmax units layer 818 to produce a reduced dimensional vector forlabeling whether an indicator of a detected feature is an indicator of atarget discontinuity. For example, with reference to FIG. 5, afterappropriate training, the reduced dimensional vector can be used tolabel features in the edge-detected seismic dataset 504 by labeling theindicator of the reflector 510 as “not a fault” and labeling theindicator of the fault 512 as a “fault.”

FIG. 9 depicts a workflow for an automated fault interpretation system.After first acquiring seismic data such as a set of two-dimensionalseismic datasets that can be stacked to form a three-dimensional seismicvolume 902, an edge-detection method such as one incorporating a phasecongruency operation can be used to process the two-dimensional seismicdatasets to identify candidate discontinuities and determine indicatorsfor the candidate discontinuities. With reference to FIG. 3, the edgedetection method can be similar to or the same as the edge detectionmethod described for block 308. Use of the edge detection algorithm 904can generate the two-dimensional seismic datasets forming anedge-extracted volume 906. A convolutional neural network 908 canprocess the edge-extracted volume 906 to denoise the edge-extractedvolume 906 into an interpreted seismic volume 910. With reference toFIG. 4, the convolutional neural network 908 can incorporate similar orthe same operations as those described for blocks 404-416 to generatethe interpreted seismic volume 910. In some embodiments, labeleddatasets can include data assigned to pixels of a seismic dataset,wherein the data contain binary labels such as “fault” or “no fault” foreach pixel of the seismic dataset.

Based on the values of the interpreted seismic volume 910, a datamapping/migration method 912 can be applied to combine thefractures/faults identified in the interpreted seismic volume 910 togenerate a combined interpreted seismic volume 920. In some embodiments,data mapping can involve overlaying the output of the denoised volumeonto the interpreted seismic volume. In some embodiments, intermediateprocessing can occur before or as a part of the data mapping to narrow,connect, extend, or otherwise clarify interpreted fault geometry in thedenoised volume.

FIG. 10 depicts a comparison between an expert-labeled seismic datasetand an automated fault interpretation system-labeled seismic dataset.FIG. 10 depicts an expert-labeled dataset 1002, wherein each of thelines represent a labeled geological fracture or fault. After trainingthe convolutional neural network of an automated fault interpretationsystem with the expert-labeled dataset 1002, an automated faultinterpretation system-labeled dataset 1050 can provide similar resultsas the expert-labeled dataset 1002, wherein similarity can be defined asa less than 10% pixel difference between the two images.

EXAMPLE DRILLING SYSTEM

FIG. 11 depicts an example drilling system near a fault. FIG. 11 depictsa drilling system 1100. The drilling system 1100 includes a drilling rig1101 located at the surface 1102 of a borehole 1103. The initialposition of the borehole 1103 and various operational parameters (e.g.drilling speed, weight on bit, drilling fluid pump rate, drillingdirection, drilling fluid composition) for drilling can be selectedbased on the results of the operations using an automated faultinterpretation system (as described above). For example, with referenceto FIG. 3, the position of the borehole 1103 can be selected to avoidfaults identified using a set of indicators of discontinuities providedby the operations disclosed in blocks 304-344. The drill string 1104 canbe operated for drilling the borehole 1103 through the subsurfaceformation 1132 with the bottomhole assembly (BHA).

The BHA includes a drill bit 1130 at the downhole end of the drillstring 1104. The drill bit 1130 is in the vicinity of a fault 1175,wherein the position of fault 1175 is determined by an automated faultinterpretation system. The BHA and the drill bit 1130 can be coupled tocomputing system 1150, which can operate the drill bit 1130 as well asreceive data based on the sensors attached to the BHA. The drill bit1130 can be operated to create the borehole 1103 by penetrating thesurface 1102 and subsurface formation 1132. In some embodiments, adrilling plan can call for the drill bit 1130 to stop drilling whenwithin a range of the fault 1175. By increasing the accuracy of theseismic interpretation, the drill bit 1130 can more safely and easilyavoid penetrating through the fault 1175. For example, sensors on theBHA can transmit a signal to the computing system 1150 that the drillbit is near the fault 1175, and the computing system can stop the drillbit 1130.

EXAMPLE WELLBORE SYSTEM

FIG. 12 depicts an example wellbore system near a fault. A wellboresystem 1200 depicted in FIG. 12 comprises a wellbore 1204 penetrating atleast a portion of a subterranean formation 1202. The wellbore 1204comprises one or more injection points 1214 where one or more fluids canbe injected from the wellbore 1204 into the subterranean formation 1202.The subterranean formation 1202 can comprise pores initially saturatedwith reservoir fluids (e.g., oil, gas, and/or water). In certainembodiments, the wellbore system 1200 can be treated by the injection ofa fracturing fluid, acid, or proppant at one or more injection points1214 in the wellbore 1204. In certain embodiments, the one or moreinjection points 1214 can correspond to injection points 1214 in acasing of the wellbore 1204. When fluid enters the subterraneanformation 1202 at the injection points 1214, one or more fractures 1218can be opened. In certain embodiments, a diverting agent can enter theinjection point 1214 and restrict the flow of further fluid. In someembodiments, the fracturing fluid can comprise a diverter.

As depicted in FIG. 12, the subterranean formation 1202 includes atleast one fracture network 1208 connected to the wellbore 1204. Thefracture network 1208 shown in FIG. 12 contains a number of junctionsand fractures 1218. The number of junctions and fractures can varydrastically and/or unpredictably depending on the specificcharacteristics of the subterranean formation 1202. For example, thefracture network 1208 can comprise on the order of thousands offractures 1218 to tens of thousands of fractures 1218. In someembodiments, these fractures can be within range of a fault 1275,wherein the position, orientation, and/or shape of the fault 1275 isdetermined using an automated fault interpretation system. For example,with reference to FIG. 3, using the operations disclosed in blocks304-344, an indicator of the fault 1275 can be determined and used tofind the position of the fault 1275.

In certain embodiments, an operational parameter can comprise one ormore wellbore treatment controls and/or wellbore production controls.These operational parameters can be selected to avoid faults identifiedin the operations described above. In certain embodiments, the wellboretreatment controls can characterize a treatment operation for a wellbore1204 penetrating at least a portion of a subterranean formation 1202. Incertain embodiments, the operational parameters can include, but are notlimited to an amount of acid, fracturing fluid or diverter pumped intothe wellbore system 1200, a proppant concentration pumped into thewellbore system 1200, a proppant size used during pumping into thewellbore system 1200, a wellbore pressure at the injection points 1214,a fluid or diverter flow rate at the wellbore inlet 1210, the pressureat the wellbore inlet 1210, a duration of a acidizing/stimulationtreatment, a diverter particle diameter, and any combination thereof. Incertain embodiments, in response to calculations determining that afracturing or acidization operation may damage or perforate the fault1275, an operational parameter can be altered to prevent thedamage/perforation from occurring. For example, a computer system candetermine that a set of operational parameters will result in damagingthe fault 1275, and, in response, reduce a fluid flow rate at thesurface 1206.

In certain embodiments, the one or more operational parameters can bechanged in response to real-time measurements. In some embodiments,real-time measurements can comprise pressure measurements, flow ratemeasurements, and seismic measurements. In certain embodiments,real-time measurements can be obtained from one or more wellsite datasources or sensors in acoustic communication with the subterraneanformation 1202. Wellsite data sources can include, but are not limitedto, flow sensors, pressure sensors, thermocouples, and any othersuitable measurement apparatus. In certain embodiments, wellsite datasources can be positioned at the surface, on a downhole tool, in thewellbore 1204 or in fractures 1218. Pressure measurements can, forexample, be obtained from a pressure sensor at a surface of the wellbore1204.

EXAMPLE COMPUTING SYSTEM

FIG. 13 depicts an example computer system. A computer device 1300includes a processor 1301 (possibly including multiple processors,multiple cores, multiple nodes, and/or implementing multi-threading,etc.). The computer device 1300 includes a memory 1307. The memory 1307can be system memory (e.g., one or more of cache, SRAM, DRAM, zerocapacitor RAM, Twin Transistor RAM, eDRAM, EDO RAM, DDR RAM, EEPROM,NRAM, RRAM, SONOS, PRAM, etc.) or any one or more of the above alreadydescribed possible realizations of machine-readable media. The computerdevice 1300 also includes a bus 1303 (e.g., PCI, ISA, PCI-Express,HyperTransport® bus, InfiniBand® bus, NuBus, etc.) and a networkinterface 1305 (e.g., a Fiber Channel interface, an Ethernet interface,an internet small computer system interface, SONET interface, wirelessinterface, etc.).

In some embodiments, the computer device 1300 includes an edge detector1311. The edge detector 1311 can perform one or more operations fordetecting the candidate discontinuities of seismic dataset, includingoperations to apply a phase congruency operation. The neural networkprocessor 1312 can perform one or more operations for classifying andfiltering a seismic dataset, including operations to apply aconvolutional neural network to classify indicators of candidatediscontinuities as indicators of faults/fractures and removingindicators of candidate discontinuities that are not indicators offaults/fractures from a labeled subset of indicators of discontinuities.The operational parameter controller 1313 can perform one or moreoperations for controlling a drilling system or a wellbore system,including controlling a drill bit or fluid pump rate. Any one of thepreviously described functionalities can be partially (or entirely)implemented in hardware and/or on the processor 1301. For example, thefunctionality can be implemented with an application specific integratedcircuit, in logic implemented in the processor 1301, in a co-processoron a peripheral device or card, etc. Further, realizations can includefewer or additional components not illustrated in FIG. 13 (e.g., videocards, audio cards, additional network interfaces, peripheral devices,etc.). The processor 1301 and the network interface 1305 are coupled tothe bus 1303. Although illustrated as being coupled to the bus 1303, thememory 1307 can be coupled to the processor 1301. The computer device1300 can be integrated into component(s) of the drill pipe downholeand/or be a separate device at the surface that is communicativelycoupled to the BHA downhole for controlling and processing signals (asdescribed herein).

As will be appreciated, aspects of the disclosure can be embodied as asystem, method or program code/instructions stored in one or moremachine-readable media. Accordingly, aspects can take the form ofhardware, software (including firmware, resident software, micro-code,etc.), or a combination of software and hardware aspects that can allgenerally be referred to herein as a “circuit,” “module” or “system.”The functionality presented as individual modules/units in the exampleillustrations can be organized differently in accordance with any one ofplatform (operating system and/or hardware), application ecosystem,interfaces, programmer preferences, programming language, administratorpreferences, etc.

Any combination of one or more machine-readable medium(s) can beutilized. The machine-readable medium can be a machine-readable signalmedium or a machine-readable storage medium. A machine-readable storagemedium can be, for example, but not limited to, a system, apparatus, ordevice, that employs any one of or combination of electronic, magnetic,optical, electromagnetic, infrared, or semiconductor technology to storeprogram code. More specific examples (a non-exhaustive list) of themachine-readable storage medium would include the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, amachine-readable storage medium can be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device. A machine-readablestorage medium is not a machine-readable signal medium.

A machine-readable signal medium can include a propagated data signalwith machine readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal can takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Amachine-readable signal medium can be any machine readable medium thatis not a machine-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a machine-readable medium can be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thedisclosure can be written in any combination of one or more programminglanguages, including an object oriented programming language such as theJava® programming language, C++ or the like; a dynamic programminglanguage such as Python; a scripting language such as Perl programminglanguage or PowerShell script language; and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code can execute entirely on astand-alone machine, can execute in a distributed manner across multiplemachines, and can execute on one machine while providing results and oraccepting input on another machine.

The program code/instructions can also be stored in a machine-readablemedium that can direct a machine to function in a particular manner,such that the instructions stored in the machine-readable medium producean article of manufacture including instructions which implement thefunction/act specified in the flowchart and/or block diagram block orblocks.

Use of the phrase “at least one of” preceding a list with theconjunction “and” should not be treated as an exclusive list and shouldnot be construed as a list of categories with one item from eachcategory, unless specifically stated otherwise. A clause that recites“at least one of A, B, and C” can be infringed with only one of thelisted items, multiple of the listed items, and one or more of the itemsin the list and another item not listed.

EXAMPLE EMBODIMENTS

Example embodiments include the following:

Embodiment 1: A method for determining a position of a geologicalfeature in a formation comprising: acquiring a seismic dataset, whereinthe seismic dataset is based on signals of one or more seismic sensorsto receive waves from within the formation; determining a set ofindicators of candidate discontinuities in the formation based on theseismic dataset; labeling a subset of the set of indicators of candidatediscontinuities using a neural network with a label based on the set ofindicators of candidate discontinuities, wherein the label distinguishesan indicator of a candidate discontinuity between being an indicator ofa target discontinuity or being an indicator of a non-targetdiscontinuity; and determining the position of the geological feature inthe formation, wherein the geological feature in the formation isassociated with at least one target discontinuity based on the subset ofthe set of indicators of candidate discontinuities.

Embodiment 2: The method of Embodiment 1, wherein the determining theset of indicators of candidate discontinuities based on the seismicdataset comprises applying a phase congruency operation on the seismicdataset.

Embodiment 3: The method of Embodiments 1 or 2, wherein labeling thesubset of the set of indicators of candidate discontinuities comprises:generating a convoluted dataset based on one or more convolution layersof the neural network, wherein the one or more convolution layers areapplied to at least one of the seismic dataset and the set of indicatorsof candidate discontinuities; and labeling the subset of the set ofindicators of candidate discontinuities using one or more rectifiedlinear units layers of the neural network based on the convoluteddataset.

Embodiment 4: The method of any of Embodiments 1-3, wherein determiningthe set of indicators of candidate discontinuities comprisespartitioning the seismic dataset into seismic subsamples.

Embodiment 5: The method of any of Embodiments 1-4, wherein labeling thesubset of the set of indicators of candidate discontinuities comprisesperforming a binary classification operation using the neural network.

Embodiment 6: The method of any of Embodiments 1-5, wherein thegeological feature is at least one of a fracture and a geological fault.

Embodiment 7: The method of any of Embodiments 1-6, wherein labeling thesubset of the set of indicators of candidate discontinuities comprisesdistinguishing an indicator of a candidate discontinuity between beingan indicator of a target discontinuity or an indicator of a signalreflector from the formation.

Embodiment 8: One or more non-transitory machine-readable mediacomprising program code for determining a position of a geologicalfeature in a formation, the program code to: acquire a seismic dataset,wherein the seismic dataset is based on signals of one or more seismicsensors to receive waves from within a formation; determine a set ofindicators of candidate discontinuities in the formation based on theseismic dataset; label a subset of the set of indicators of candidatediscontinuities using a neural network with a label based on the set ofindicators of candidate discontinuities, wherein the label distinguishesan indicator of a candidate discontinuity between being an indicator ofa target discontinuity or being an indicator of a non-targetdiscontinuity; and determine the position of the geological feature inthe formation, wherein the geological feature in the formation isassociated with at least one target discontinuity based on the subset ofthe set of indicators of candidate discontinuities.

Embodiment 9: The machine-readable media of Embodiment 8, wherein theprogram code to determine the set of indicators of candidatediscontinuities based on the seismic dataset comprises program code toapply a phase congruency operation on the seismic dataset.

Embodiment 10: The machine-readable media of Embodiments 8 or 9, whereinthe program code to label the subset of the set of indicators ofcandidate discontinuities comprises program code to: generate aconvoluted dataset based on one or more convolution layers of the neuralnetwork, wherein the one or more convolution layers are applied to atleast one of the seismic dataset and the set of indicators of candidatediscontinuities; and label the subset of the set of indicators ofcandidate discontinuities using one or more rectified linear unitslayers of the neural network based on the convoluted dataset.

Embodiment 11: The machine-readable media of any of Embodiments 8-10,wherein the program code to determine the set of indicators of candidatediscontinuities comprises program code to partition the seismic datasetinto seismic subsamples.

Embodiment 12: The machine-readable media of any of Embodiments 8-11,wherein the program code to label the subset of the set of indicators ofcandidate discontinuities comprises program code to perform a binaryclassification operation using the neural network.

Embodiment 13: The machine-readable media of any of Embodiments 8-12,wherein the geological feature is at least one of a fracture and ageological fault.

Embodiment 14: The machine-readable media of any of Embodiments 8-13,wherein the program code to label the subset of the set of indicators ofcandidate discontinuities comprises program code to distinguish anindicator of a candidate discontinuity between being an indicator of atarget discontinuity or an indicator of a signal reflector from theformation.

Embodiment 15: An apparatus comprising: one or more seismic sensors toreceive waves from within a formation; a processor; and amachine-readable medium having program code executable by the processorto cause the apparatus to, acquire a seismic dataset, wherein theseismic dataset is based on signals of the one or more seismic sensors,determine a set of indicators of candidate discontinuities in theformation based on the seismic dataset, label a subset of the set ofindicators of candidate discontinuities using a neural network with alabel based on the set of indicators of candidate discontinuities,wherein the label distinguishes an indicator of a candidatediscontinuity between being an indicator of a target discontinuity orbeing an indicator of a non-target discontinuity, and determine aposition of a geological feature in the formation, wherein thegeological feature in the formation is associated with at least onetarget discontinuity based on the subset of the set of indicators ofcandidate discontinuities.

Embodiment 16: The apparatus of Embodiment 15, wherein the program codeto determine the set of indicators of candidate discontinuities based onthe seismic dataset comprises program code to apply a phase congruencyoperation on the seismic dataset.

Embodiment 17: The apparatus of Embodiments 15 or 16, wherein theprogram code to label the subset of the set of indicators of candidatediscontinuities comprises program code to: generate a convoluted datasetbased on one or more convolution layers of the neural network, whereinthe one or more convolution layers are applied to at least one of theseismic dataset and the set of indicators of candidate discontinuities;and label the subset of the set of indicators of candidatediscontinuities using one or more rectified linear units layers of theneural network based on the convoluted dataset.

Embodiment 18: The apparatus of any of Embodiments 15-17, wherein theprogram code to determine the set of indicators of candidatediscontinuities comprises program code to partition the seismic datasetinto seismic subsamples.

Embodiment 19: The apparatus of any of Embodiments 15-18, wherein theprogram code to label the subset of the set of indicators of candidatediscontinuities comprises program code to perform a binaryclassification operation using the neural network.

Embodiment 20: The apparatus of any of Embodiments 15-19, wherein thegeological feature is at least one of a fracture and a geological fault.

What is claimed is:
 1. A method for determining a position of ageological feature in a formation comprising: acquiring a seismicdataset, wherein the seismic dataset is based on signals of one or moreseismic sensors to receive waves from within the formation; determininga set of indicators of candidate discontinuities in the formation basedon the seismic dataset; labeling a subset of the set of indicators ofcandidate discontinuities using a neural network with a label based onthe set of indicators of candidate discontinuities, wherein the labeldistinguishes an indicator of a candidate discontinuity between being anindicator of a target discontinuity or being an indicator of anon-target discontinuity; and determining the position of the geologicalfeature in the formation, wherein the geological feature in theformation is associated with at least one target discontinuity based onthe subset of the set of indicators of candidate discontinuities.
 2. Themethod of claim 1, wherein the determining the set of indicators ofcandidate discontinuities based on the seismic dataset comprisesapplying a phase congruency operation on the seismic dataset.
 3. Themethod of claim 1, wherein labeling the subset of the set of indicatorsof candidate discontinuities comprises: generating a convoluted datasetbased on one or more convolution layers of the neural network, whereinthe one or more convolution layers are applied to at least one of theseismic dataset and the set of indicators of candidate discontinuities;and labeling the subset of the set of indicators of candidatediscontinuities using one or more rectified linear units layers of theneural network based on the convoluted dataset.
 4. The method of claim1, wherein determining the set of indicators of candidatediscontinuities comprises partitioning the seismic dataset into seismicsubsamples.
 5. The method of claim 1, wherein labeling the subset of theset of indicators of candidate discontinuities comprises performing abinary classification operation using the neural network.
 6. The methodof claim 1, wherein the geological feature is at least one of a fractureand a geological fault.
 7. The method of claim 6, wherein labeling thesubset of the set of indicators of candidate discontinuities comprisesdistinguishing an indicator of a candidate discontinuity between beingan indicator of a target discontinuity or an indicator of a signalreflector from the formation.
 8. One or more non-transitorymachine-readable media comprising program code for determining aposition of a geological feature in a formation, the program code to:acquire a seismic dataset, wherein the seismic dataset is based onsignals of one or more seismic sensors to receive waves from within aformation; determine a set of indicators of candidate discontinuities inthe formation based on the seismic dataset; label a subset of the set ofindicators of candidate discontinuities using a neural network with alabel based on the set of indicators of candidate discontinuities,wherein the label distinguishes an indicator of a candidatediscontinuity between being an indicator of a target discontinuity orbeing an indicator of a non-target discontinuity; and determine theposition of the geological feature in the formation, wherein thegeological feature in the formation is associated with at least onetarget discontinuity based on the subset of the set of indicators ofcandidate discontinuities.
 9. The machine-readable media of claim 8,wherein the program code to determine the set of indicators of candidatediscontinuities based on the seismic dataset comprises program code toapply a phase congruency operation on the seismic dataset.
 10. Themachine-readable media of claim 8, wherein the program code to label thesubset of the set of indicators of candidate discontinuities comprisesprogram code to: generate a convoluted dataset based on one or moreconvolution layers of the neural network, wherein the one or moreconvolution layers are applied to at least one of the seismic datasetand the set of indicators of candidate discontinuities; and label thesubset of the set of indicators of candidate discontinuities using oneor more rectified linear units layers of the neural network based on theconvoluted dataset.
 11. The machine-readable media of claim 8, whereinthe program code to determine the set of indicators of candidatediscontinuities comprises program code to partition the seismic datasetinto seismic subsamples.
 12. The machine-readable media of claim 8,wherein the program code to label the subset of the set of indicators ofcandidate discontinuities comprises program code to perform a binaryclassification operation using the neural network.
 13. Themachine-readable media of claim 8, wherein the geological feature is atleast one of a fracture and a geological fault.
 14. The machine-readablemedia of claim 8, wherein the program code to label the subset of theset of indicators of candidate discontinuities comprises program code todistinguish an indicator of a candidate discontinuity between being anindicator of a target discontinuity or an indicator of a signalreflector from the formation.
 15. An apparatus comprising: one or moreseismic sensors to receive waves from within a formation; a processor;and a machine-readable medium having program code executable by theprocessor to cause the apparatus to, acquire a seismic dataset, whereinthe seismic dataset is based on signals of the one or more seismicsensors, determine a set of indicators of candidate discontinuities inthe formation based on the seismic dataset, label a subset of the set ofindicators of candidate discontinuities using a neural network with alabel based on the set of indicators of candidate discontinuities,wherein the label distinguishes an indicator of a candidatediscontinuity between being an indicator of a target discontinuity orbeing an indicator of a non-target discontinuity, and determine aposition of a geological feature in the formation, wherein thegeological feature in the formation is associated with at least onetarget discontinuity based on the subset of the set of indicators ofcandidate discontinuities.
 16. The apparatus of claim 15, wherein theprogram code to determine the set of indicators of candidatediscontinuities based on the seismic dataset comprises program code toapply a phase congruency operation on the seismic dataset.
 17. Theapparatus of claim 15, wherein the program code to label the subset ofthe set of indicators of candidate discontinuities comprises programcode to: generate a convoluted dataset based on one or more convolutionlayers of the neural network, wherein the one or more convolution layersare applied to at least one of the seismic dataset and the set ofindicators of candidate discontinuities; and label the subset of the setof indicators of candidate discontinuities using one or more rectifiedlinear units layers of the neural network based on the convoluteddataset.
 18. The apparatus of claim 15, wherein the program code todetermine the set of indicators of candidate discontinuities comprisesprogram code to partition the seismic dataset into seismic subsamples.19. The apparatus of claim 15, wherein the program code to label thesubset of the set of indicators of candidate discontinuities comprisesprogram code to perform a binary classification operation using theneural network.
 20. The apparatus of claim 15, wherein the geologicalfeature is at least one of a fracture and a geological fault.